Particularly in medical research, various imaging methods are used in the analysis of tissue structures and brain signals. Modern physiological recording and imaging is made using sensor arrays comprising several tens or hundreds of sensors performing parallel recording of the neural activity. Such multi-channel devices are used for example in magnetoencephalography (MEG), where MEG means the measurement and analysis of magnetic fields generated by the electric activity of the brain. Electroencephalography (EEG) measures electric potentials in desired parts of the patient. A further multichannel measurement application is magnetic resonance imaging (MRI), simply referred as magnetic imaging. The MRI is applicable to different parts of the body and it uses multiple receiver coils.
It is typical of the imaging methods that a large set of measurement channels and related measurement sensors are needed therein. The signal recorded by each individual sensor in these multichannel devices contains both information on the neural activity (or on precession of nuclear magnetization in case of MRI), and interference from environmental sources, and artifacts and random noise related to the sensor technology. Since the neurological electric and magnetic fields are very weak, such sensor noise tends to hamper the detection of the interesting neurological signals. Therefore, the sensors must have very low-noise characteristics and they should be situated close to the measured object. The typical noise level of a measurement sensor measuring a magnetic field is of the order of a few femtoteslas. It is characteristic to the biomagnetic measurements that the magnetic flux densities to be measured are very low (for example of the order of 10 . . . 1000 fT), and the external interference fields prevailing in the measurement situation may be quite large in comparison to the flux densities, even of the order of 1 . . . 10 μT. The estimation of the portion of different interference signals in the overall measurement signal and the elimination of the effect of interferences from measurement results is thus extremely essential in multichannel biomagnetic measurement methods.
Additionally, artifacts (such as sudden spikes and jumps) related to individual channels may be misinterpreted to represent real neural activity, such as interictal epileptic spiking, for example. Therefore, from the point of view of clinical utilization of these multichannel technologies, both artifacts and random sensor noise should be removed or damped as much as possible.
A calculatory method used in the analysis of measured signals and to reject environmental interference from multichannel MEG signals is the so-called Signal Space Separation method (abbreviated as the SSS method), which is discussed for example in patent publication FI 115324. The SSS method is currently quite amply used in the art. It is a calculatory method for separating multichannel measurement signal information, on the basis of the locations of the sources, into various signal bases, i.e. subspaces that are linearly independent of one another. The SSS is purely based on the geometry of the sensor assembly and natural laws. The calculation according to the principle of the SSS begins by applying Maxwell's equations describing the relations of electric and magnetic fields. In the SSS method, it is possible to separate the magnetic fields generated by the useful sources (such as the brain) and the magnetic fields originating from external interference sources. In other words, series developments are calculated in the SSS method using division according to sources located in different sites. It may be referred to as a source modeling method for the multichannel measurement signal in a volume where the magnetic fields to be determined are irrotational and sourceless. The SSS method does not need advanced information about the types or locations of the different signal sources but it works correctly in the cases of different types of signal sources, also when examined as a function of time even when the location and/or intensity of the sources changes. In the calculation according to the SSS method, the geometry of the sensor assembly thus plays an important role. Associated to the geometry is also the fact that, in addition to the location, the orientation of the sensors significantly affects the measured signal because the magnetic field is a direction-dependent quantity.
Describing in other words the SSS method in general, an n-dimensional basis, the so-called SSS-basis, is formed for the N-dimensional signal space of the N-channel MEG device. The number of basis vectors n is smaller than N. The basis vectors are chosen so that each of them corresponds to a physically possible magnetic field shape in a source free space. Furthermore, based on the asymptotic behaviour of the corresponding magnetic potential functions, when r→0 and r→∞, these basis vectors are divided into two groups: the ones that correspond to magnetic fields arising from sources inside of the MEG sensor helmet, and the others corresponding to magnetic fields arising from sources in the environment, outside of the MEG helmet. The former group contains the field of interest, arising from neuromagnetic sources, and the latter group contains the environmental interference contribution to the signals that is wanted to be removed. The basic idea of the SSS method is to simply leave out from the SSS-basis representation of the recorded signals the components belonging to the latter group, and thus, the desired biomagnetic signal is achieved more accurately.
An advantage of the SSS method is that it can observe all interferences regardless of time and place. Since the calculation is made independently for each sample, it observes the changing situations regardless of whether the interference sources are changing inside or outside the measurement area, which can be e.g. a magnetically shielded room. A problem of the SSS method is that it is sensitive to calibration errors. This means that, for example, a signal deviation measured by one of the sensors may not be due to interference but a small unrecognized deviation in the position or direction (angle of its axis) of the sensor.
The main problem of the prior art is that, even by using solely the SSS method, any artifacts or peculiarities emerging in any of the sensors or measurement channels are left outside of the SSS-based signal modeling, and are therefore still hard to handle in the biomagnetic measurements and their calculatory analysis. In other words, in case a channel starts to behave in an odd manner, the noise level will rise and weaken the measurement results' quality significantly.